Optimal Boundary SVM Incremental Learning Algorithm

DOI

10.2991/isccca.2013.33How to use a DOI?

Keywords

supportt vector mechine, increamental learning, KKT condition

Abstract

Support vectors(SVs) can’t be selected completely in support vector machine(SVM) incremental, resulting incremental learning process can’t be sustained. In order to solve this problem, the article proposes optimal boundary SVM incremental learning algorithm. Based on in-depth analysis of the trend of the classification surface and make use of the KKT conditions, selecting the border of the vectors include the support vectors to participate SVM incremental learning. The experiment shows that the algorithm can be completely covered the support vectors and have the identical result with the classic support vector machine, it also saves lots of time. Therefore it can provide the conditions for future large sample classification and incremental learning sustainability.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jian Cao
AU  - Shiyu Sun
AU  - Xiusheng Duan
PY  - 2013/02
DA  - 2013/02
TI  - Optimal Boundary SVM Incremental Learning Algorithm
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
PB  - Atlantis Press
SP  - 132
EP  - 135
SN  - 1951-6851
UR  - https://doi.org/10.2991/isccca.2013.33
DO  - 10.2991/isccca.2013.33
ID  - Cao2013/02
ER  -